Abstract：K-means clustering algorithm is one of the most commonly used algorithms in data mining.When performing massive data analysis,the calculation time of K-Means mean clustering is also proportional to the amount of data to be processed,so the larger the amount of data,the greater the calculation.In order to improve the performance of the algorithm,this paper designs a multi-level parallel k-means algorithm optimization method based on CUDA model.Based on serial analysis of the K-Means algorithm,it is modified into a parallel algorithm.And the multi-level performance optimization of the parallel computing part including thread block level,thread level,instruction level,and bit level.First,in the kernel that calculates the distance between the sample points and the cluster center,the idea of matrix multiplication is used to improve the algorithm performance.Then,the block,the number of threads,the number of instructions executed by each thread,and the number of bits are analyzed and optimized step by step.With the reasonable use of computing resources and storage resources,the algorithm's computing performance is increased as much as possible to achieve the optimal clustering effect.Finally,through a number of experiments,the method of this paper is simulated and verified to verify its feasibility.The experimental results show that the optimized algorithm improves the calculation speed while ensuring the accuracy of the experimental results.Compared with the general parallel K-Means algorithm,the maximum speed-up is 39.7% and average speed-up is 22.3%,while the resource occupancy rate has decreased.
2021, Vol. 42 
